Stimulations
Gijsje
2/16/2021
## Warning: package 'BiocGenerics' was built under R version 4.0.5## Warning: package 'GenomeInfoDb' was built under R version 4.0.5load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata2.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
setDT(genes_tpm_filt, keep.rownames = "ensembl")
genes_tpm_filt$ensembl
res_name = merge(genes_tpm_filt, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensemblmarker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
dim (marker_expression)
head (marker_expression)Expression of aging markers (based on MiGA) across stimulations
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:484])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_filt$Stimulation <- as.factor(metadata_filt$Stimulation)
metadata_filt2 <- metadata_filt[order(metadata_filt$Stimulation, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted2 <- scale(sorted2)
ann <- data.frame(metadata_filt2$Stimulation)
colnames(ann) <- c('stimulation')
colours <- list('stimulation' = c('ununstim' = 'red2', 'unstim' = 'royalblue', 'TNFa' = 'navy', 'R848'='green', 'LPS'='pink', 'IL4'='yellow', 'DEX'='orange', 'ATP'='brown', 'IFNy'='purple'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))#pdf(file = "~/Documents/MiGASti/Databases/Stim_markers.pdf", width = 100,
#height = 6)
colPalette <- colorRampPalette(rev(brewer.pal(11, "RdYlBu")))(496)
Heatmap(sorted2,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = T,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
column_split = rep(c(metadata_filt2$Stimulation)),
top_annotation = colAnn)
#dev.off()
LPS versus baseline
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "unstim" | Stimulation == "LPS")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:258])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$Stimulation <- as.factor(metadata_cultured$Stimulation)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$Stimulation, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- scale(sorted)
sorted2 <- t(sorted2)
ann <- data.frame(metadata_filt2$Stimulation)
colnames(ann) <- c('stimulation')
colours <- list('stimulation' = c('unstim' = 'royalblue', 'LPS' = 'navy'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
# pdf(paste0(work_plots, "HM_markers_255s.pdf"), width = 10, height = 6)
colPalette <- colorRampPalette(rev(brewer.pal(10, "RdYlBu")))(10)
Heatmap(sorted2,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
column_split = rep(c(metadata_filt2$Stimulation)),
top_annotation = colAnn)
Mean values per gene LPS versus baseline
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "unstim" | Stimulation == "LPS")
metadata_filt <- metadata_cultured
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_filt$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
markers2 <- markers[!duplicated(markers$symbol), ]
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:263])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_filt$Stimulation <- as.factor(metadata_filt$Stimulation)
metadata_filt2 <- metadata_filt[order(metadata_filt$Stimulation, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
Stimulation <- metadata_filt2$Stimulation
df <- rbind(sorted_scale, Stimulation)
library(dplyr)
df2 <- t(df)
df3 <- as.data.frame(df2)
setDT(df3)
df4 <- df3 %>%
group_by(Stimulation) %>%
summarise_all("mean")
sorted_cor = as.matrix(df4[,2:1687])
rownames(sorted_cor) <- c("LPS", "unstim")
sorted_final <- t(sorted_cor)
# pdf(paste0(work_plots, "HM_markers_255s.pdf"), width = 10, height = 6)
colPalette <- colorRampPalette(rev(brewer.pal(10, "RdYlBu")))(70)
Heatmap(sorted_final,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = T,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = T,
show_column_names = T,
show_row_names = F)
IFNy versus baseline samples
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "unstim" | Stimulation == "IFNy")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:212])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$Stimulation <- as.factor(metadata_cultured$Stimulation)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$Stimulation, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- scale(sorted)
sorted2 <- t(sorted2)
ann <- data.frame(metadata_filt2$Stimulation)
colnames(ann) <- c('stimulation')
colours <- list('stimulation' = c('unstim' = 'royalblue', 'IFNy' = 'navy'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
# pdf(paste0(work_plots, "HM_markers_255s.pdf"), width = 10, height = 6)
colPalette <- colorRampPalette(rev(brewer.pal(10, "RdYlBu")))(10)
Heatmap(sorted2,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
column_split = rep(c(metadata_filt2$Stimulation)),
top_annotation = colAnn)
Mean values aging genes between IFNy and baseline
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "unstim" | Stimulation == "IFNy")
metadata_filt <- metadata_cultured
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_filt$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
markers2 <- markers[!duplicated(markers$symbol), ]
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:217])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_filt$Stimulation <- as.factor(metadata_filt$Stimulation)
metadata_filt2 <- metadata_filt[order(metadata_filt$Stimulation, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
Stimulation <- metadata_filt2$Stimulation
df <- rbind(sorted_scale, Stimulation)
library(dplyr)
df2 <- t(df)
df3 <- as.data.frame(df2)
setDT(df3)
df4 <- df3 %>%
group_by(Stimulation) %>%
summarise_all("mean")
sorted_cor = as.matrix(df4[,2:1687])
rownames(sorted_cor) <- c("IFNy", "unstim")
sorted_final <- t(sorted_cor)
# pdf(paste0(work_plots, "HM_markers_255s.pdf"), width = 10, height = 6)
colPalette <- colorRampPalette(rev(brewer.pal(10, "RdYlBu")))(70)
Heatmap(sorted_final,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = T,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = T,
show_column_names = T,
show_row_names = F)
IFNy samples across aging
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "IFNy")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:85])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$age, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age <- metadata_filt2$age
ann <- data.frame(metadata_filt2$Stimulation)
colnames(ann) <- c('stimulation')
colours <- list('stimulation' = c('unstim' = 'royalblue', 'IFNy' = 'navy'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
col_age = as.data.frame(metadata_filt2[c("age")] ,)
ha1 = HeatmapAnnotation(df = col_age,
col = list(age = colorRamp2(c(20, 100), c("white", "red"))))
colPalette <- colorRampPalette(rev(brewer.pal(7, "RdYlBu")))(10)
p1 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = ha1)Baseline samples across aging
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "unstim")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:138])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$age, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age <- metadata_filt2$age
col_age = as.data.frame(metadata_filt2[c("age")] ,)
ha1 = HeatmapAnnotation(df = col_age,
col = list(age = colorRamp2(c(20, 100), c("white", "red"))))
colPalette <- colorRampPalette(rev(brewer.pal(7, "RdYlBu")))(10)
p2 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = ha1)LPS samples across aging
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "LPS")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:131])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$age, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age <- metadata_filt2$age
col_age = as.data.frame(metadata_filt2[c("age")] ,)
ha1 = HeatmapAnnotation(df = col_age,
col = list(age = colorRamp2(c(20, 100), c("white", "red"))))
colPalette <- colorRampPalette(rev(brewer.pal(7, "RdYlBu")))(10)
p3 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = ha1)Baseline, LPS and IFNy across aging
p2 + p3 + p1
Baseline samples across aging top 50 genes
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "unstim")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
marker_expression = marker_expression[1:50 ,]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:138])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$age, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age <- metadata_filt2$age
col_age = as.data.frame(metadata_filt2[c("age")] ,)
ha1 = HeatmapAnnotation(df = col_age,
col = list(age = colorRamp2(c(20, 100), c("white", "red"))))
colPalette <- colorRampPalette(rev(brewer.pal(7, "RdYlBu")))(10)
p2 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = ha1)LPS samples across aging top 50 genes
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "LPS")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
marker_expression = marker_expression[1:50,]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:131])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$age, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age <- metadata_filt2$age
col_age = as.data.frame(metadata_filt2[c("age")] ,)
ha1 = HeatmapAnnotation(df = col_age,
col = list(age = colorRamp2(c(20, 100), c("white", "red"))))
colPalette <- colorRampPalette(rev(brewer.pal(7, "RdYlBu")))(10)
p3 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = ha1)IFNy samples across aging top 50 genes
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "IFNy")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
marker_expression = marker_expression[1:50,]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:85])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured[order(metadata_cultured$age, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt2),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age <- metadata_filt2$age
ann <- data.frame(metadata_filt2$Stimulation)
colnames(ann) <- c('stimulation')
colours <- list('stimulation' = c('unstim' = 'royalblue', 'IFNy' = 'navy'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
col_age = as.data.frame(metadata_filt2[c("age")] ,)
ha1 = HeatmapAnnotation(df = col_age,
col = list(age = colorRamp2(c(20, 100), c("white", "red"))))
colPalette <- colorRampPalette(rev(brewer.pal(7, "RdYlBu")))(10)
p1 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = ha1)Expression aging genes in baseline, LPS and IFNy top 50 genes in each condition
p2 + p3 + p1
Baseline samples across aging top 100 genes
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "unstim")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
marker_expression = marker_expression[1:25 ,]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:138])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured %>%
mutate(quantile = ntile(age, 5))
table(metadata_filt2$quantile)1 2 3 4 5 27 27 26 26 26
metadata_filt2$quantile <- as.factor(metadata_filt2$quantile)
metadata_filt3 <- metadata_filt2[order(metadata_filt2$quantile, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt3),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age_quantiles <- metadata_filt3$quantile
ann <- data.frame(metadata_filt3$quantile)
colnames(ann) <- c('age')
colours <- list('age' = c('1' = 'royalblue', '2'='pink', '3'='green', '4'='red', '5'='navy'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
colPalette <- colorRampPalette(rev(brewer.pal(10, "RdYlBu")))(79)
p2 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = F,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = colAnn)LPS samples across aging top 50 genes
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "LPS")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
marker_expression = marker_expression[1:25,]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:131])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured %>%
mutate(quantile = ntile(age, 5))
table(metadata_filt2$quantile)1 2 3 4 5 25 25 25 25 25
metadata_filt2$quantile <- as.factor(metadata_filt2$quantile)
metadata_filt3 <- metadata_filt2[order(metadata_filt2$quantile, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt3),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age_quantiles <- metadata_filt3$quantile
ann <- data.frame(metadata_filt3$quantile)
colnames(ann) <- c('age')
colours <- list('age' = c('1' = 'royalblue', '2'='pink', '3'='green', '4'='red', '5'='navy'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
colPalette <- colorRampPalette(rev(brewer.pal(10, "RdYlBu")))(79)
p3 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = T,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = colAnn)IFNy samples top 100 genes age quantiles
load("~/Documents/MiGASti/Databases/gene_matrix.RData")
metadata <- read.table("~/Documents/MiGASti/Databases/metadata.txt")
#set rownames to Sample
row.names(metadata) <- metadata$Sample
setwd("~/Documents/MiGASti/Databases")
exclude <- read.table("samples2remove2.txt")
exclude <- exclude$x
genes_tpm_filt = genes_tpm[, !colnames(genes_tpm) %in% exclude]
#Excludes the samples from filters.
#dim(genes_tpm_filt)
metadata_filt = metadata[ !(rownames(metadata) %in% exclude), ]
length(metadata_filt)[1] 38
#check for expression of markers in dataset
markers = "~/Documents/MiGA/Revision/AgeMiGA.xlsx"
markers = read_excel(markers, col_names = TRUE)
markers = as.data.frame(markers)
gencode_30 = read.table("~/Documents/MiGASti/Databases/ens.geneid.gencode.v30")
colnames(gencode_30) = c("ensembl","symbol")
genes_tpm_filt = log2((genes_tpm_filt) + 1)
genes_tpm_filt <- as.data.frame(genes_tpm_filt)
#remove ununstim samples in metadata
metadata_cultured <- subset(metadata_filt, Stimulation == "IFNy")
#remove samples in genes counts datafile
genes_tpm_cultured <- genes_tpm_filt[,metadata_cultured$Sample]
setDT(genes_tpm_cultured, keep.rownames = "ensembl")
res_name = merge(genes_tpm_cultured, gencode_30, by="ensembl")
rownames(res_name) = res_name$ensembl
marker_expression = merge(res_name, markers, by ="symbol")
marker_expression <- marker_expression[!duplicated(marker_expression$symbol), ]
marker_expression = marker_expression[1:25,]
rownames(marker_expression) = marker_expression$symbol
marker_expression$ensembl <- NULL
df_num = as.matrix(marker_expression[,2:85])
rownames(df_num) = marker_expression$symbol
#plot the genes based on samples
metadata_cultured$age <- as.numeric(metadata_cultured$age)
metadata_filt2 <- metadata_cultured %>%
mutate(quantile = ntile(age, 5))
table(metadata_filt2$quantile)1 2 3 4 5 16 16 16 16 15
metadata_filt2$quantile <- as.factor(metadata_filt2$quantile)
metadata_filt3 <- metadata_filt2[order(metadata_filt2$quantile, decreasing = T), ]
df <- t(df_num)
sorted <- df[rownames(metadata_filt3),]
sorted2 <- t(sorted)
sorted3 <- as.data.frame(sorted2)
sorted4 <- mutate_all(sorted3, function(x) as.numeric(as.character(x)))
sorted_scale <- scale(sorted4)
age_quantiles <- metadata_filt3$quantile
ann <- data.frame(metadata_filt3$quantile)
colnames(ann) <- c('age')
colours <- list('age' = c('1' = 'royalblue', '2'='pink', '3'='green', '4'='red', '5'='navy'))
colAnn <- HeatmapAnnotation(df = ann,
which = 'col',
col = colours,
annotation_width = unit(c(1, 4), 'cm'),
gap = unit(1, 'mm'))
colPalette <- colorRampPalette(rev(brewer.pal(10, "RdYlBu")))(79)
p1 <- Heatmap(sorted_scale,
col = colPalette,
name = "log2(TPM+1)", # Legend title
cluster_rows = T,
cluster_columns = F,
# row_names_gp = gpar(fontsize = 6), # Text size for row names)
show_column_dend = F,
show_column_names = F,
show_row_names = F,
top_annotation = colAnn)Expression aging genes in baseline, LPS and IFNy top 100 genes age quantiles
p2 + p3 + p1